Accession Number:

ADA300331

Title:

A Comparison of the Performance of Non-Parametric Classifiers with Gaussian Maximum Likelihood for the Classification of Multispectral Remotely Sensed Data.

Descriptive Note:

Master's thesis,

Corporate Author:

AIR FORCE ACADEMY COLORADO SPRINGS CO

Personal Author(s):

Report Date:

1995-10-20

Pagination or Media Count:

256.0

Abstract:

This study compares the performance of two non-parametric classifiers and Gaussian Maximum Likelihood GML for the classification of LANDSAT TM 30-meter resolution six-band data. The mathematical assumptions made in developing GML are valid if the pixels that constitute the training classes are normally distributed. Since it requires a model of the data, GML is termed a parametric classifier. Of current interest are new classification methodologies that make no assumptions about the statistical distribution of the pixels in the training class these approaches are termed non-parametric classifiers. This study will compare the n-Dimensional Probability Density Function nPDF essentially a projection technique that reduces data dimensionality, and an advanced neural network that utilizes fuzzy-set mathematics, the Fuzzy ARTMAP, to the traditional GML approach to image classification. The different approaches will be compared for statistical classification accuracy and computational efficiency. AN

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE